Summary of Study ST000396
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000309. The data can be accessed directly via it's Project DOI: 10.21228/M86G6V This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
| Study ID | ST000396 |
| Study Title | Lung Cancer Plasma Discovery |
| Study Summary | Recently, major efforts have been directed toward early detection of lung cancer through low-dose computed tomography (LDCT) scanning. Data from the National Lung Screening Trial (NLST) suggest that yearly screening with thoracic LDCT scanning for high-risk current and former smokers reduces lung cancer mortality by 20% and total mortality by 7%. However, issues including indeterminate nodules detected by LDCT and radiation exposure impact the practicality of LDCT-based screening on a national and global basis. A blood-based biomarker or multiplexed marker panel that could complement LDCT would represent a major advance in implementing lung cancer screening. Efforts to develop blood-based biomarkers for lung cancer early detection using a variety of methodologies are currently ongoing. Proteomic studies have led to the identification of several candidate markers including pro-surfactantproteinB(pro-SFTPB), a target of a lineage-survival oncogene in lung cancer, NKX2-1.Validation studies using blood samples collected at the time of LDCT screening for lung cancer substantiated the performance of pro-SFTPB. Multivariable logistic regression models were used to evaluate the predictive ability of pro-SFTPB. The area under the curve (AUC) values of the full model with and without pro-SFTPB were 0.741 (95% CI, 0.696 to 0.783) and 0.669 (95%CI, 0.620 to 0.717), respectively (difference in AUC, P_.001). Single markers are unlikely to have sufficient performance for implementation in a screening setting, hence the need to explore several discovery platforms to identify markers that provide complementary performance. Metabolomics represents a global unbiased approach to the profiling of small molecules and has been established as a platform for biomarker discovery for a variety of human biofluids and tissues. Here we used an untargeted liquid chromatography/mass spectrometry (MS) metabolomics approach to identify metabolites that distinguish human sera collected before the diagnosis of lung cancer from matched control sera in a prospective cohort of highrisk patients from the Beta-Carotene and Retinol Efficacy Trial (CARET). |
| Institute | University of California, Davis |
| Department | Genome and Biomedical Sciences Facility |
| Laboratory | WCMC Metabolomics Core |
| Last Name | Fiehn |
| First Name | Oliver |
| Address | 1315 Genome and Biomedical Sciences Facility, 451 Health Sciences Drive, Davis, CA 95616 |
| ofiehn@ucdavis.edu | |
| Phone | (530) 754-8258 |
| Submit Date | 2016-05-10 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | cdf |
| Analysis Type Detail | GC-MS |
| Release Date | 2016-06-18 |
| Release Version | 2 |
| Release Comments | Updated study design factors |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN000633 |
|---|---|
| Chromatography ID | CH000458 |
| MS ID | MS000566 |
| Analysis type | MS |
| Chromatography type | GC |
| Chromatography system | Agilent 6890N |
| Column | Restek Corporation Rtx-5Sil MS |
| MS Type | EI |
| MS instrument type | GC Ion Trap |
| MS instrument name | Varian 210-MS GC Ion Trap |
| Ion Mode | POSITIVE |
| Units | counts |
